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Case studies of arts organizations leveraging technology through new and novel approaches, research updates from AMTLab contributors, and monthly summaries of technological happenings in the arts management world.

Museums in the United States according to data from IMLS Museum Count survey. http://bit.ly/11ejwae

Using data to design marketing campaigns is great. Using
data as an accountability tool to report back to our funders is also great.
Even if we’re not leveraging it to its full potential (chances
are we’re not), most of us would agree on the last two statements.

It’s when we start talking about incorporating data-driven-decision
making to our programming that things start to get complicated. Programming
directors support their decisions on a thoughtful understanding of the underlying
values of a particular art form, values that theorists have spent years trying
to understand. Data, it seems, has no place in the business of the aesthetic
experience.

I will spend the next couple of months trying to prove us
wrong. By no means will I attempt to show that data analysis can replace the
role of curators and artistic directors; instead, I will try to understand as an arts manager when, and how, we can
incorporate spatial data to develop helpful variables in the programming
decision making process.

So what is spatial data? It’s simply information that
relates to a specific geographic location; it can cover a large area, -a state
or a country- refer to a specific point in the map -the location of a school-
or anything in between. Naturally, this is more beneficial in some fields; I
will draw examples of how this would be useful for public art projects since they
are, by nature, embedded in a physical location, and from community outreach
programs in an effort to show how we can be more proactive reaching the
audience that they will benefit the most.

Unlike other projects involving data analysis, we already have
high quality datasets available through private and government agencies;
because these are the planning stages, we work with demographics and statistics
about our potential audience, not the
people we already reach. Of course, if we have comparable past examples,
collecting and using our own data would enrich our efforts, but it’s not absolutely
necessary.

Spatial location can help us visualize the idiosyncrasies of
our towns and neighborhoods. So, can we understand if the Rubber Duck
will work the same way it did in Hong Kong when we bring it to Pittsburgh? Can
it help us decide if we should place a permanent or a temporary piece in that
particularly high traffic spot downtown? Are we maximizing our audience when we
cluster works in a sculpture park, or would we be better serving our mission if
we scattered them around a main avenue? Data can be used not only to measure
the success of our programming after the fact, but also to make the most educated
decision about what is optimal when we’re faced with a set of choices.

I would love to hear your comments and questions, please post
them below or feel free to email editor@amt-lab.org